# Load packages
library(pacman)
pacman::p_load(glue)
pacman::p_load(DT)
pacman::p_load(magick)
This document shows the results of the meta-analysis of the studies: E_MEXP_1425, GSE2508, GSE29718, GSE64567, GSE92405, GSE141432,
GSE205668 para el contraste Ob.F - C.F.
Taking a p-value adjusted for BH < 0.05 we found 310 genes
significativos.
DT = get(load("C:/Users/roxya/OneDrive/Documentos/01Master_bioinformatica/00TFM/05Paper/AnalysisGit/Data/MA/Obesity/IOF/Meta-analysis_IOF_DF.RData"))
datatable(DT, options = list(caption = "Meta-analysis results.",
scrollX = TRUE),
filter = "top")
Here are the characteristic figures of the meta-analysis for the
significant genes taking a p-value adjusted for BH <
0.05.
# Plot function
grid_img <- function(list_fig, dir, patt, dim = 300){
# Abrimos todas las filas:
forest_plots = c()
funnel_plots = c()
influ_plots = c()
for (fig in list_fig){
f1 = glue("{fig}forest.svg")
out1 <- magick::image_read(paste0(dir, "/", f1))
forest_plots = c(forest_plots, image_scale(out1, dim))
f2 = glue("{fig}funnel.svg")
out2 <- magick::image_read(paste0(dir,"/", f2))
funnel_plots = c(funnel_plots, image_scale(out2, dim))
f3 = glue("{fig}influence.svg")
out3 <- magick::image_read(paste0(dir,"/", f3))
influ_plots = c(influ_plots, image_scale(out3, dim))
}
lim = length(forest_plots)
grid = NULL
for ( i in seq(list_fig)){
a = forest_plots[[i]]
b = funnel_plots[[i]]
c = influ_plots[[i]]
fila <- image_append(c(a, b, c))
if(is.null(grid)){
grid = fila
}else{
grid = image_append(c(grid,fila), stack = TRUE)
}
}
return(grid)
}